Three-dimensional (3-D) volume imaging has proved to be a valuable diagnostic tool that offers significant advantages over earlier two-dimensional (2-D) radiographic imaging techniques for evaluating the condition of internal structures and organs. 3-D imaging of a patient or other subject has been made possible by a number of advancements, including the development of high-speed imaging detectors, such as digital radiography (DR) detectors that enable multiple images to be taken in rapid succession.
Conventional computed tomography CT scanners direct a fan-shaped X-ray beam through the patient or other subject and toward a one-dimensional detector, reconstructing a succession of single slices to obtain a volume or 3-D image. Cone-beam computed tomography or CBCT scanning makes it possible to improve image capture and processing speeds by directing a cone-beam source toward the subject and obtaining the image on a flat-panel X-ray detector. In cone-beam computed tomography scanning, a 3-D image is reconstructed from numerous individual scan projections, each taken at a different angle, whose image data is aligned and processed in order to generate and present data as a collection of volume pixels or voxels.
The processing of CBCT data for obtaining images requires some type of reconstruction algorithm. Various types of image reconstruction have been proposed, generally classified as (i) exact or approximate, or (ii) iterative or analytic. Exact cone-beam reconstruction algorithms, based on theoretical work of a number of researchers, require that the following sufficient condition be satisfied: “on every plane that intersects the imaged object there exists at least one cone-beam source”. The widely used Grangeat algorithm, familiar to those skilled in CBCT image processing, is limited to circular scanning trajectory and spherical objects. Only recently, with generalization of the Grangeat formula, is exact reconstruction possible in spiral/helical trajectory with longitudinally truncated data.
Despite advances in exact methods (i, above), approximate methods (ii) continue to be more widely used. Chief among these CBCT reconstruction approaches and familiar to those skilled in the CT imaging arts are the Feldkamp/Davis/Kress (FDK) based algorithms.
Although 3-D images of diagnostic quality can be generated using CBCT systems and technology, however, a number of technical challenges remain.
Increased resolution in the digital image domain (e.g., 2D projection images) is desirable. There is a compelling need for improved methods for increased resolution techniques in the volume DR image reconstruction processing.